樊薇：Separated Sparse Representation Model for Bearing Fault Detection
2017-10-24
时 间：2017年10月27日，周五13:30
地 点：阳澄湖校区交通大楼一楼学术报告厅
报告人：樊薇
题 目：Sparse Representation Model for Bearing Fault Detection
摘 要：
Vibration signal from a rolling bearing with localized faults always contains periodic transients and background noise. This talk will present a novel fault detection technique by utilizing the sparsity of the transients. The separated sparse representation (SSR) model with a tunable separation time parameter is constructed. In the implementation of the model, a B-spline dictionary is adopted to represent the transient due to its inherent ability to model sparsity and its impressive flexibility, and then the model is solved by split augmented Lagrangian shrinkage algorithm (SALSA). The power value calculated by the reconstructed signal will reach the maximum when the separation time parameter is the same as the true fault period, which is proposed as a criterion to detect the fault period. The performance of the proposed method is compared with conventional methods in the simulation studies. Finally, case studies are used to illustrate the effectiveness of the proposed methodology in fault period detection.
报告人简介：
Wei Fan received her bachelor’s degree and master’s degree both from Soochow University. She is currently working towards the Ph.D. degree in Department of Systems Engineering and Engineering Management at City University of Hong Kong. Her research interests focus on statistical process control, signal processing and machinery fault diagnosis.
